Digital Translation - Volume 13, Issue 2, 2026

Abstract

This study examines the performance of various machine translation (MT) systems and a generative AI (GenAI) translation in the context of game text translation using data from the popular racing game Need for Speed: Unbound (Criterion Games 2022). Focusing on factors such as ambiguity, contextual disambiguation, stylistic variations, domain specific knowledge, and technical entities (e.g. placeholders), the analysis compares human translations with outputs from Google Translate, DeepL, Papago, and ChatGPT. Three representative examples from a game script are examined, revealing that while MT and GenAI systems can preserve lexical content, they often fail to capture critical nuances and contextual meanings that are crucial for interactive gaming environments. The findings highlight the need for integrating additional contextual information and domain-specific post-editing to improve MT and GenAI translation quality for translated game texts, contributing to the broader discussion on enhancing interactive media translation.

Abstract

Recent legislative mandates have expanded language access in government services, yet research related to the integration of machine translation (MT) remains limited. This study evaluates the quality and efficiency of Neural Machine Translation (NMT) systems (DeepL, Google Translate) and Large Language Models (LLMs) (GPT-4) in translating government-based legal documents from English to Spanish. Methodologically, the study involved twenty-seven professional translators who conducted human translation (HT), machine translation post-editing (MTPE), and quality evaluation. Translation quality was measured using an adapted Multidimensional Quality Metrics (MQM) framework, while technical and temporal post-editing efforts were measured via keylogging software. The findings indicate that (a) MTPE significantly reduces translation completion time compared to HT; (b) GPT-4, an LLM, achieves higher overall quality scores than traditional NMT engines, including DeepL and Google Translate; and (c) MTPE and HT perform similarly in overall quality. The study underscores the potential of LLM-based translation technologies, combined with professional human post-editing, as a high-quality and efficient solution to meeting growing language access demands in government contexts. These findings offer critical insights for policymakers and translation professionals in public services.

Abstract

The integration of artificial intelligence (AI) technologies is transforming workflows and creating new scenarios within the language services industry, demanding the development of new skills and competencies among professionals. This position paper provides an overview of the evolving profile of language specialists in AI-mediated dubbing and voice-over processes. It examines client expectations, specific tasks assigned to translation and language professionals, and the implications for training programs and curriculum development. To this end, an AI dubbing linguist competence profile is proposed, informed by tool experimentation, stakeholder conversations, and observations of industry dynamics from practitioner, trainer, and researcher perspectives. This profile aims to offer insights to language service providers (LSPs), tech developers, academic trainers, and both aspiring and practicing language professionals seeking to navigate this evolving landscape. By identifying the nuanced, creative, and cross-disciplinary responsibilities of linguists in AI-driven workflows, the paper aims to dispel any misconceptions that such solutions diminish the role of the linguist/translator. Instead, it points to an expanded and more nuanced professional profile requiring multi-layered expertise across domains, positioning linguists as key contributors to these hybrid dubbing processes.

This article reviews Machine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence978-3-98554-045-7978-3-96110-348-5

This article reviews Hybrid Workflows in Translation: Integrating GenAI into Translator Training9781032860473

This article reviews Terminology Management for Translators9781032299068